High Performance Text Mining for Translator

我们建议建立一个知识提供者，将寻找，整合和提供AIready， 通过生物医学文献的高性能文本挖掘的BioLink兼容模型。 译者目前对生物医学文献的挖掘存在的问题， 解决方案包括：（1）框架可扩展性和基准测试方面弱点， 整合和验证新的文本挖掘方法困难;（2）许可问题 不充分支持FAIR（和TLC）的软件、术语和其他资源 最佳实践;（3）仅处理PubMed标题和摘要，而不是全文出版物;（4） 翻译者使用较旧的NLP技术，性能相对较差;（5）缺乏 社区对错误和其他问题的反馈机制;（6）缺乏持续的 更新以添加来自新出版物的知识;（7）输出 简单和模糊，未能反映科学文献中表达的内容的丰富性。

We propose to build a knowledge provider that will seek out, integrate and provide AIready, BioLink-compatible models via high-performance text-mining of the biomedical literature. Problems with Translator’s current mining of the biomedical literature that we intend to solve include: (1) weaknesses in framework extensibility and benchmarking that make integrating and validating new text-mining approaches difficult; (2) problematic licensing of software, terminologies and other resources that do not adequately support FAIR (and TLC) best practices; (3) processing only PubMed titles and abstracts, not full text publications; (4) Translator’s use of older NLP technology with relatively poor performance; (5) lack of a mechanism for community feedback regarding errors and other problems; (6) lack of continuous updates to add knowledge from new publications; (7) output knowledge representation that is simplistic and vague, failing to reflect the richness of what is expressed in scientific documents.